MATLABonColab.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# MATLAB on Google Colab"
      ],
      "metadata": {
        "id": "2TmaEkaoz5zS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the example that will be covered in this notebook:\n",
        "https://www.mathworks.com/help/deeplearning/ug/time-series-forecasting-using-deep-learning.html\n",
        "\n",
        "- Start by changing the runtime to a GPU\n",
        "- Execute the following bash script to get MATLAB:\n",
        "https://gist.github.com/yanndebray/e267617c78a3f24c875cb57570bdd3b9\n",
        "- Add more toolboxes as needed to the line `mpm install --products=MATLAB Parallel_Computing_Toolbox Deep_Learning_Toolbox Statistics_and_Machine_Learning_Toolbox`\n",
        "https://www.mathworks.com/help/install/ug/mpminstall.html\n",
        "(you can get the list from this page, replace the spaces by underscores_ https://www.mathworks.com/products.html)\n",
        "\n",
        "\n",
        "More resources:\n",
        "- https://blogs.mathworks.com/deep-learning/2023/07/26/matlab-with-tensorflow-from-your-jupyter-notebook/\n",
        "- https://www.mathworks.com/help/deeplearning/ref/exportnetworktotensorflow.html"
      ],
      "metadata": {
        "id": "ptMhFqD9rAbL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkpsPCA-0iyO",
        "outputId": "89ab0ec5-ddbf-4f44-cdd2-caed7f5eb826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Aug 12 20:04:16 2025       \n",
            "+-----------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |\n",
            "|-----------------------------------------+------------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                        |               MIG M. |\n",
            "|=========================================+========================+======================|\n",
            "|   0  Tesla T4                       Off |   00000000:00:04.0 Off |                    0 |\n",
            "| N/A   41C    P8              9W /   70W |       0MiB /  15360MiB |      0%      Default |\n",
            "|                                         |                        |                  N/A |\n",
            "+-----------------------------------------+------------------------+----------------------+\n",
            "                                                                                         \n",
            "+-----------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                              |\n",
            "|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |\n",
            "|        ID   ID                                                               Usage      |\n",
            "|=========================================================================================|\n",
            "|  No running processes found                                                             |\n",
            "+-----------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://gist.githubusercontent.com/yanndebray/e267617c78a3f24c875cb57570bdd3b9/raw/c840eae32fb8be4b0b90fc852b43f719ec89bbff/get_matlab_on_colab.sh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oazYQ1MsP7e3",
        "outputId": "8ad1ec1a-4ae2-4979-b006-05ac6be88245"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-08-12 19:40:37--  https://gist.githubusercontent.com/yanndebray/e267617c78a3f24c875cb57570bdd3b9/raw/1b9dce314770da627ecaf162bc1a3ecc5748a999/get_matlab_on_colab.sh\n",
            "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 755 [text/plain]\n",
            "Saving to: ‘get_matlab_on_colab.sh’\n",
            "\n",
            "get_matlab_on_colab 100%[===================>]     755  --.-KB/s    in 0s      \n",
            "\n",
            "2025-08-12 19:40:38 (35.2 MB/s) - ‘get_matlab_on_colab.sh’ saved [755/755]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!chmod +x get_matlab_on_colab.sh"
      ],
      "metadata": {
        "id": "soGhLkvCP8qK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !./get_matlab_on_colab.sh # run in the terminal"
      ],
      "metadata": {
        "collapsed": true,
        "id": "1B5HLf-_QBAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "29etVw0R5QX7",
        "outputId": "b4bb18c1-96d6-4f75-a0ef-f202e50655ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mWarning: This function may stop working due to changes in browser security.\n",
            "Try `serve_kernel_port_as_iframe` instead. \u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, text, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port);\n",
              "    const anchor = document.createElement('a');\n",
              "    anchor.href = new URL(path, url).toString();\n",
              "    anchor.target = '_blank';\n",
              "    anchor.setAttribute('data-href', url + path);\n",
              "    anchor.textContent = text;\n",
              "    element.appendChild(anchor);\n",
              "  })(3000, \"/\", \"https://localhost:3000/\", window.element)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import output\n",
        "output.serve_kernel_port_as_window(3000, path='/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import forecastTF\n",
        "model = forecastTF.load_model()"
      ],
      "metadata": {
        "id": "xHLZ_Ef8IAVf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model.layers\n",
        "# model.input_shape\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "CcoAuxpxIPLg",
        "outputId": "40bd842f-0ab7-47cf-90b3-74d8d099ec8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_ (\u001b[38;5;33mLSTM\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m67,584\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ fc_ (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)        │           \u001b[38;5;34m387\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">67,584</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ fc_ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">387</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m67,971\u001b[0m (265.51 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">67,971</span> (265.51 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m67,971\u001b[0m (265.51 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">67,971</span> (265.51 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On the MATLAB side, share the engine with the command [matlab.engine.shareEngine](https://www.mathworks.com/help/matlab/ref/matlab.engine.shareengine.html)"
      ],
      "metadata": {
        "id": "DyB_aavbuzF6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matlab.engine\n",
        "eng = matlab.engine.find_matlab()[0] # attach to running session\n",
        "m = matlab.engine.connect_matlab(eng)\n",
        "# m.who()"
      ],
      "metadata": {
        "id": "68G_TMHyIwzk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "X = m.workspace['X']\n",
        "X = np.array(X)\n",
        "X = np.expand_dims(X, axis=0)\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIw-KsyCJmdj",
        "outputId": "07fa4ef5-2362-4945-9c26-07bbc64de3dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 191, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = model.predict(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDd5CYrha4C1",
        "outputId": "acc09803-29c9-451a-ecd2-491e910daf4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py:237: UserWarning: The structure of `inputs` doesn't match the expected structure.\n",
            "Expected: ['keras_tensor']\n",
            "Received: inputs=Tensor(shape=(1, 191, 3))\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 56s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfQWcnyVchPx",
        "outputId": "a95f5c19-a316-4832-83a9-99ba2dbdf605"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 191, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(X[0,:,0])\n",
        "plt.plot(Y[0,:,0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "z5wqW1ltcdO6",
        "outputId": "f20425c4-a456-4760-9234-66c54f2b9f39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78611a6854d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}